In preventive maintenance and mission planning, it is important to calculate the likelihood of failures in a monitored system as symptoms (i.e., evidence) are observed. Since many failures frequently have overlapping evidence, it is often the case that ambiguity in fault reasoning will exist when trying to find the root cause failure.
In some currently available health management systems, all the evidence is collected in a single database and it is assumed that there is only one failure. In some cases, however, there are two or more failures in a monitored system. In this case, the health management system will still only indicate a single failure although conflated or indeterminate.
Other currently available health management systems allow for any number of faults, however the computation is exponentially expensive. Alternative fault condition splitting rubrics require an inordinate amount of computing power and computing overhead. Thus, it is desirable to split simultaneous occurring faults into their individual fault conditions while reducing the computing overhead necessary to do so.